The domestic ferret (Mustela putorius furo) has long been a popular animal model for evaluating viral pathogenesis and transmission as well as the efficacy of candidate countermeasures. Without question, the ferret has been most widely implemented for modeling respiratory viruses, particularly influenza viruses; however, in recent years, it has gained attention as a novel animal model for characterizing filovirus infections. Although ferrets appear resistant to infection and disease caused by Marburg and Ravn viruses, they are highly susceptible to lethal disease caused by Ebola, Sudan, Bundibugyo, and Reston viruses. Notably, unlike the immunocompetent rodent models of filovirus infection, ferrets are susceptible to lethal disease caused by wild-type viruses, and they recapitulate many aspects of human filovirus disease, including systemic virus replication, coagulation abnormalities, and a dysregulated immune response. Along with the stringency with which they reproduce Ebola disease, their relatively small size and availability make ferrets an attractive choice for countermeasure evaluation and pathogenesis modeling. Indeed, they are so far the only small animal model available for Bundibugyo virus. Nevertheless, ferrets do have their limitations, including the lack of commercially available reagents to dissect host responses and their unproven predictive value in therapeutic evaluation. Although the use of the ferret model in ebolavirus research has been consistent over the last few years, its widespread use and utility remains to be fully proven. This review provides a comprehensive overview of the ferret models of filovirus infection and perspective on their ongoing use in pathogenesis modeling and countermeasure evaluation.The pioneer transcription factor (TF) PU.1 controls hematopoietic cell fate by decompacting stem cell heterochromatin and allowing nonpioneer TFs to enter otherwise inaccessible genomic sites. PU.1 deficiency fatally arrests lymphopoiesis and myelopoiesis in mice, but human congenital PU.1 disorders have not previously been described. We studied six unrelated agammaglobulinemic patients, each harboring a heterozygous mutation (four de novo, two unphased) of SPI1, the gene encoding PU.1. Affected patients lacked circulating B cells and possessed few conventional dendritic cells. Introducing disease-similar SPI1 mutations into human hematopoietic stem and progenitor cells impaired early in vitro B cell and myeloid cell differentiation. Patient SPI1 mutations encoded destabilized PU.1 proteins unable to nuclear localize or bind target DNA. In PU.1-haploinsufficient pro-B cell lines, euchromatin was less accessible to nonpioneer TFs critical for B cell development, and gene expression patterns associated with the pro- to pre-B cell transition were undermined. Our findings molecularly describe a novel form of agammaglobulinemia and underscore PU.1's critical, dose-dependent role as a hematopoietic euchromatin gatekeeper.A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computational methods have been proposed for identifying potential adverse DDIs, there is still room for improvement. Existing methods are not explicitly based on the knowledge that DDIs are fundamentally caused by chemical substructure interactions instead of whole drugs' chemical structures. Furthermore, most of existing methods rely on manually engineered molecular representation, which is limited by the domain expert's knowledge.We propose substructure-substructure interaction-drug-drug interaction (SSI-DDI), a deep learning framework, which operates directly on the raw molecular graph representations of drugs for richer feature extraction; and, most importantly, breaks the DDI prediction task between two drugs down to identifying pairwise interactions between their respective substructures. SSI-DDI is evaluated on real-world data and improves DDI prediction performance compared to state-of-the-art methods. Source code is freely available at https//github.com/kanz76/SSI-DDI.Infectious diseases can cause psychological changes in patients. https://www.selleckchem.com/products/pf-04965842.html This study aimed to evaluate the prevalence and related risk factors for anxiety and depression in patients with COVID-19.
A cross-sectional study was performed on patients with COVID-19 admitted to the Sino-French New City branch of Wuhan Tongji Hospital from January to February 2020. The Zung Self-Rating Anxiety and Depression Scales were used to evaluate the prevalence of anxiety and depression. Demographic, clinical, and sociological data were also collected. Multivariable logistic regression analysis was used to identify independent risk factors of anxiety and depression in patients with COVID-19.
In the current study, 183 patients were enrolled (mean age = 53 ± 9 years; 41.1% women). The prevalences of anxiety and depression were 56.3% and 39.3%, respectively. Logistic regression analysis revealed that older age, female sex, being divorced or widowed, COVID-19 disease duration, renal disease, and depression were identified as independent risk factors for anxiety in patients with COVID-19. Factors that were associated with depression were female sex, being widowed, COVID-19 disease duration, and anxiety.
This study demonstrates a high prevalence of anxiety and depression in patients with COVID-19 at the peak of the epidemic in Wuhan, China. The identification of demographic, clinical, and social factors may help identify health care professionals to provide psychological care as part of treatment for patients with COVID-19 and other life-threatening infectious diseases.
This study demonstrates a high prevalence of anxiety and depression in patients with COVID-19 at the peak of the epidemic in Wuhan, China. The identification of demographic, clinical, and social factors may help identify health care professionals to provide psychological care as part of treatment for patients with COVID-19 and other life-threatening infectious diseases.